{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"/Users/elvis/Downloads/202006091427.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edp       object\n",
       "kl       float64\n",
       "yhl      float64\n",
       "ykpw     float64\n",
       "jkpw     float64\n",
       "jkhsl    float64\n",
       "jkjsl    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edp</th>\n",
       "      <th>kl</th>\n",
       "      <th>yhl</th>\n",
       "      <th>ykpw</th>\n",
       "      <th>jkpw</th>\n",
       "      <th>jkhsl</th>\n",
       "      <th>jkjsl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>29062.0</td>\n",
       "      <td>10.049613</td>\n",
       "      <td>0.488028</td>\n",
       "      <td>18.353152</td>\n",
       "      <td>78.639349</td>\n",
       "      <td>111.534660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>29600.0</td>\n",
       "      <td>13.577006</td>\n",
       "      <td>0.470296</td>\n",
       "      <td>9.912257</td>\n",
       "      <td>68.420083</td>\n",
       "      <td>95.245887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>38538.0</td>\n",
       "      <td>22.566779</td>\n",
       "      <td>0.587473</td>\n",
       "      <td>15.058608</td>\n",
       "      <td>66.456518</td>\n",
       "      <td>150.457756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>39021.0</td>\n",
       "      <td>17.529110</td>\n",
       "      <td>0.563089</td>\n",
       "      <td>14.670673</td>\n",
       "      <td>71.644790</td>\n",
       "      <td>157.420011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>36638.0</td>\n",
       "      <td>18.819982</td>\n",
       "      <td>0.537677</td>\n",
       "      <td>12.133169</td>\n",
       "      <td>71.111553</td>\n",
       "      <td>140.085494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>35980.0</td>\n",
       "      <td>12.702773</td>\n",
       "      <td>0.535464</td>\n",
       "      <td>15.229425</td>\n",
       "      <td>77.874016</td>\n",
       "      <td>150.031923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>21375.0</td>\n",
       "      <td>10.491409</td>\n",
       "      <td>0.612026</td>\n",
       "      <td>10.809107</td>\n",
       "      <td>79.664912</td>\n",
       "      <td>104.218036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>13886.0</td>\n",
       "      <td>11.626707</td>\n",
       "      <td>0.564062</td>\n",
       "      <td>13.764030</td>\n",
       "      <td>84.275835</td>\n",
       "      <td>66.009554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-01-09</td>\n",
       "      <td>24986.0</td>\n",
       "      <td>23.075400</td>\n",
       "      <td>0.508414</td>\n",
       "      <td>8.148327</td>\n",
       "      <td>52.773447</td>\n",
       "      <td>67.039271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>36721.0</td>\n",
       "      <td>21.099143</td>\n",
       "      <td>0.544100</td>\n",
       "      <td>14.452727</td>\n",
       "      <td>62.618335</td>\n",
       "      <td>125.110681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          edp       kl        yhl      ykpw       jkpw      jkhsl       jkjsl\n",
       "0  2020-01-01  29062.0  10.049613  0.488028  18.353152  78.639349  111.534660\n",
       "1  2020-01-02  29600.0  13.577006  0.470296   9.912257  68.420083   95.245887\n",
       "2  2020-01-03  38538.0  22.566779  0.587473  15.058608  66.456518  150.457756\n",
       "3  2020-01-04  39021.0  17.529110  0.563089  14.670673  71.644790  157.420011\n",
       "4  2020-01-05  36638.0  18.819982  0.537677  12.133169  71.111553  140.085494\n",
       "5  2020-01-06  35980.0  12.702773  0.535464  15.229425  77.874016  150.031923\n",
       "6  2020-01-07  21375.0  10.491409  0.612026  10.809107  79.664912  104.218036\n",
       "7  2020-01-08  13886.0  11.626707  0.564062  13.764030  84.275835   66.009554\n",
       "8  2020-01-09  24986.0  23.075400  0.508414   8.148327  52.773447   67.039271\n",
       "9  2020-01-10  36721.0  21.099143  0.544100  14.452727  62.618335  125.110681"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kl      -1.321057\n",
       "yhl      0.405488\n",
       "ykpw     0.780435\n",
       "jkpw     0.316519\n",
       "jkhsl   -1.492042\n",
       "jkjsl    0.008520\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kl</th>\n",
       "      <th>yhl</th>\n",
       "      <th>ykpw</th>\n",
       "      <th>jkpw</th>\n",
       "      <th>jkhsl</th>\n",
       "      <th>jkjsl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32628.105691</td>\n",
       "      <td>13.084039</td>\n",
       "      <td>0.594543</td>\n",
       "      <td>12.946862</td>\n",
       "      <td>76.154018</td>\n",
       "      <td>149.730209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8101.101446</td>\n",
       "      <td>3.629137</td>\n",
       "      <td>0.100537</td>\n",
       "      <td>2.887071</td>\n",
       "      <td>7.253136</td>\n",
       "      <td>53.370458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2094.000000</td>\n",
       "      <td>4.751183</td>\n",
       "      <td>0.414338</td>\n",
       "      <td>5.282921</td>\n",
       "      <td>42.634599</td>\n",
       "      <td>9.920203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29377.500000</td>\n",
       "      <td>10.497508</td>\n",
       "      <td>0.520117</td>\n",
       "      <td>10.987269</td>\n",
       "      <td>72.164456</td>\n",
       "      <td>112.613139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35041.000000</td>\n",
       "      <td>12.952006</td>\n",
       "      <td>0.573612</td>\n",
       "      <td>13.043851</td>\n",
       "      <td>77.874016</td>\n",
       "      <td>148.545552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38199.500000</td>\n",
       "      <td>15.185334</td>\n",
       "      <td>0.654018</td>\n",
       "      <td>14.500542</td>\n",
       "      <td>81.376539</td>\n",
       "      <td>186.613597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42185.000000</td>\n",
       "      <td>23.075400</td>\n",
       "      <td>0.912236</td>\n",
       "      <td>21.349450</td>\n",
       "      <td>87.575213</td>\n",
       "      <td>279.271344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 kl         yhl        ykpw        jkpw       jkhsl  \\\n",
       "count    123.000000  123.000000  123.000000  123.000000  123.000000   \n",
       "mean   32628.105691   13.084039    0.594543   12.946862   76.154018   \n",
       "std     8101.101446    3.629137    0.100537    2.887071    7.253136   \n",
       "min     2094.000000    4.751183    0.414338    5.282921   42.634599   \n",
       "25%    29377.500000   10.497508    0.520117   10.987269   72.164456   \n",
       "50%    35041.000000   12.952006    0.573612   13.043851   77.874016   \n",
       "75%    38199.500000   15.185334    0.654018   14.500542   81.376539   \n",
       "max    42185.000000   23.075400    0.912236   21.349450   87.575213   \n",
       "\n",
       "            jkjsl  \n",
       "count  123.000000  \n",
       "mean   149.730209  \n",
       "std     53.370458  \n",
       "min      9.920203  \n",
       "25%    112.613139  \n",
       "50%    148.545552  \n",
       "75%    186.613597  \n",
       "max    279.271344  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kl</th>\n",
       "      <th>yhl</th>\n",
       "      <th>ykpw</th>\n",
       "      <th>jkpw</th>\n",
       "      <th>jkhsl</th>\n",
       "      <th>jkjsl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kl</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162889</td>\n",
       "      <td>0.106917</td>\n",
       "      <td>-0.052609</td>\n",
       "      <td>0.093320</td>\n",
       "      <td>0.770522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yhl</th>\n",
       "      <td>0.162889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059075</td>\n",
       "      <td>-0.085993</td>\n",
       "      <td>-0.422077</td>\n",
       "      <td>0.011358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ykpw</th>\n",
       "      <td>0.106917</td>\n",
       "      <td>0.059075</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007778</td>\n",
       "      <td>0.358893</td>\n",
       "      <td>0.647763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jkpw</th>\n",
       "      <td>-0.052609</td>\n",
       "      <td>-0.085993</td>\n",
       "      <td>0.007778</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.110721</td>\n",
       "      <td>-0.092831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jkhsl</th>\n",
       "      <td>0.093320</td>\n",
       "      <td>-0.422077</td>\n",
       "      <td>0.358893</td>\n",
       "      <td>-0.110721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.505122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jkjsl</th>\n",
       "      <td>0.770522</td>\n",
       "      <td>0.011358</td>\n",
       "      <td>0.647763</td>\n",
       "      <td>-0.092831</td>\n",
       "      <td>0.505122</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             kl       yhl      ykpw      jkpw     jkhsl     jkjsl\n",
       "kl     1.000000  0.162889  0.106917 -0.052609  0.093320  0.770522\n",
       "yhl    0.162889  1.000000  0.059075 -0.085993 -0.422077  0.011358\n",
       "ykpw   0.106917  0.059075  1.000000  0.007778  0.358893  0.647763\n",
       "jkpw  -0.052609 -0.085993  0.007778  1.000000 -0.110721 -0.092831\n",
       "jkhsl  0.093320 -0.422077  0.358893 -0.110721  1.000000  0.505122\n",
       "jkjsl  0.770522  0.011358  0.647763 -0.092831  0.505122  1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#相关性\n",
    "data.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f95dbe35880>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dbe80d90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f95dbebb250>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dbee76d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f95dbf15b20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dbf41040>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "kl          AxesSubplot(0.125,0.657941;0.227941x0.222059)\n",
       "yhl      AxesSubplot(0.398529,0.657941;0.227941x0.222059)\n",
       "ykpw     AxesSubplot(0.672059,0.657941;0.227941x0.222059)\n",
       "jkpw        AxesSubplot(0.125,0.391471;0.227941x0.222059)\n",
       "jkhsl    AxesSubplot(0.398529,0.391471;0.227941x0.222059)\n",
       "jkjsl    AxesSubplot(0.672059,0.391471;0.227941x0.222059)\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='box',subplots=True,layout=(3,3),sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f95dc684a30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dc6b0580>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dc72e9d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dc75bd00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dc793190>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dc7bf520>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f95dc7bf610>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dc916ac0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dc97e340>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dc9ab790>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dc9d8be0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dca040d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f95dca3d4c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dca68910>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dca98d90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcacf220>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcafc670>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcb2aa30>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcb621f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcb8a970>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcbc0130>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcbe98b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcc140d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcc497f0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcc72f70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcca9730>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dccd2eb0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcd07670>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcd32df0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcd675e0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcd90d60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcdc6520>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dcdefca0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dce26460>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dce4dbe0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f95dce863a0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(data, alpha=0.7, figsize=(6,6), diagonal='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "x = data[['kl', 'yhl', 'ykpw']]\n",
    "y = data[['jkpw']]\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_regression\n",
    "SelectKBest = SelectKBest(f_regression, k=2)\n",
    "bestFeature = SelectKBest.fit_transform(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SelectKBest.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['kl', 'yhl'], dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns[SelectKBest.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = data[['yhl', 'ykpw']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a17bdd60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a17e8c70>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a18f6130>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a191d5b0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(features, alpha=0.7, figsize=(6,6), diagonal='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-65-b4ee6abe01cd>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  features['标准化'+feature] = scaler.fit_transform(features[[feature]])\n",
      "<ipython-input-65-b4ee6abe01cd>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  features['标准化'+feature] = scaler.fit_transform(features[[feature]])\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a1b09730>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a1b31cd0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a1bf4190>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a1c214c0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "for feature in features.columns:\n",
    "    features['标准化'+feature] = scaler.fit_transform(features[[feature]])\n",
    "\n",
    "#散点可视化，查看特征归一化后的数据\n",
    "font={\n",
    "      'family':'SimHei'\n",
    "      }\n",
    "matplotlib.rc('font', **font)\n",
    "pd.plotting.scatter_matrix(features[['标准化yhl', '标准化ykpw']], alpha=0.7, figsize=(6,6), diagonal='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yhl</th>\n",
       "      <th>ykpw</th>\n",
       "      <th>标准化yhl</th>\n",
       "      <th>标准化ykpw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.049613</td>\n",
       "      <td>0.488028</td>\n",
       "      <td>0.289149</td>\n",
       "      <td>0.148002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.577006</td>\n",
       "      <td>0.470296</td>\n",
       "      <td>0.481648</td>\n",
       "      <td>0.112388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.566779</td>\n",
       "      <td>0.587473</td>\n",
       "      <td>0.972243</td>\n",
       "      <td>0.347732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.529110</td>\n",
       "      <td>0.563089</td>\n",
       "      <td>0.697325</td>\n",
       "      <td>0.298758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.819982</td>\n",
       "      <td>0.537677</td>\n",
       "      <td>0.767771</td>\n",
       "      <td>0.247719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>17.693505</td>\n",
       "      <td>0.579847</td>\n",
       "      <td>0.706296</td>\n",
       "      <td>0.332416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>16.413033</td>\n",
       "      <td>0.547427</td>\n",
       "      <td>0.636417</td>\n",
       "      <td>0.267302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>13.246290</td>\n",
       "      <td>0.609152</td>\n",
       "      <td>0.463600</td>\n",
       "      <td>0.391273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>15.327090</td>\n",
       "      <td>0.576766</td>\n",
       "      <td>0.577155</td>\n",
       "      <td>0.326228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>11.798630</td>\n",
       "      <td>0.630352</td>\n",
       "      <td>0.384597</td>\n",
       "      <td>0.433852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           yhl      ykpw    标准化yhl   标准化ykpw\n",
       "0    10.049613  0.488028  0.289149  0.148002\n",
       "1    13.577006  0.470296  0.481648  0.112388\n",
       "2    22.566779  0.587473  0.972243  0.347732\n",
       "3    17.529110  0.563089  0.697325  0.298758\n",
       "4    18.819982  0.537677  0.767771  0.247719\n",
       "..         ...       ...       ...       ...\n",
       "118  17.693505  0.579847  0.706296  0.332416\n",
       "119  16.413033  0.547427  0.636417  0.267302\n",
       "120  13.246290  0.609152  0.463600  0.391273\n",
       "121  15.327090  0.576766  0.577155  0.326228\n",
       "122  11.798630  0.630352  0.384597  0.433852\n",
       "\n",
       "[123 rows x 4 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(features[['标准化yhl', '标准化ykpw']],y,test_size=0.3,random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "lr = linear_model.LinearRegression()\n",
    "lr_predict = cross_val_predict(lr,x_train,y_train,cv=5)\n",
    "lr_score = cross_val_score(lr,x_train,y_train,cv=5)\n",
    "lr_meanscore = lr_score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "linear_svr = SVR(kernel = 'linear')\n",
    "linear_svr_predict = cross_val_predict(linear_svr, x_train, y_train, cv=5)\n",
    "linear_svr_score = cross_val_score(linear_svr, x_train, y_train, cv=5)\n",
    "linear_svr_meanscore = linear_svr_score.mean()\n",
    "\n",
    "poly_svr = SVR(kernel = 'poly')\n",
    "poly_svr_predict = cross_val_predict(poly_svr, x_train, y_train, cv=5)\n",
    "poly_svr_score = cross_val_score(poly_svr, x_train, y_train, cv=5)\n",
    "poly_svr_meanscore = poly_svr_score.mean()\n",
    "\n",
    "rbf_svr = SVR(kernel = 'rbf')\n",
    "rbf_svr_predict = cross_val_predict(rbf_svr, x_train, y_train, cv=5)\n",
    "rbf_svr_score = cross_val_score(rbf_svr, x_train, y_train, cv=5)\n",
    "rbf_svr_meanscore = rbf_svr_score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mean-score5')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KNN模型\n",
    "#在KNN的回归模型当中，我们没法确定n_neighbors，因此需要最优化这个参数。\n",
    "#分别计算n_neighbors=[1,2,...,19,20]\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "score=[]\n",
    "for n_neighbors in range(1,21):\n",
    "    knn = KNeighborsRegressor(n_neighbors, weights = 'uniform' )\n",
    "    knn_predict = cross_val_predict(knn, x_train, y_train, cv=5)\n",
    "    knn_score = cross_val_score(knn, x_train, y_train, cv=5)\n",
    "    knn_meanscore = knn_score.mean()\n",
    "    score.append(knn_meanscore)\n",
    "plt.plot(score)\n",
    "plt.xlabel('n-neighbors5')\n",
    "plt.ylabel('mean-score5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#从上图可以看出，随着neighbors主键增大，模型预测能力逐渐增强，但是当大于3以后，趋于下降，因此可以选择3\n",
    "n_neighbors=14\n",
    "knn = KNeighborsRegressor(n_neighbors, weights = 'uniform' )\n",
    "knn_predict = cross_val_predict(knn, x_train, y_train, cv=5)\n",
    "knn_score = cross_val_score(knn, x_train, y_train, cv=5)\n",
    "knn_meanscore = knn_score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mean-score')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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L1m/l/Mc/IXfLzqAjichBimTgenyUj/kycJGZjQQGA6+bWXsz++VdTvcAY4HNQBrFA+dSwfRvexhPXNqDFRu2c97Yj1mXlx90JBE5COZe8h/oZvYEMMbdZ0e0Q7NJQLjBgbnuPiJ0e+vxwIfuvvZgApckOzvbc3JyorU7iaJZyzZy2ZOzqF+zGs9f2YtGdaof+IdEpFyY2Rx33+8dpJGUxLPAQOBtYBvg7j4s6imjQCUR2z5buYlLnphF7eqJvHBlL5qkpQQdSUQofUk0++V77r4iStmiSiUR+778fjNDx39KSlI8z1/Zixb1U4OOJFLllVQSkQxcrwDaAoOANrFaEFIxdGpcmxeu7EVBYRGDH/uYb9ZtCTqSiJQgkoHrkcB5QD5wYehrkUPWPrMWLw7vBcB5Yz/h6zV5AScSkXAieU6iu7sPc/fH3P0yQNNjSKm1yajJxKuOIikhjvMf/4Qvv98cdCQR2Y9In7g+38xamtmFFN+SKlJqLeqnMvGqo0hNSuCCcZ/w2cr9PlQvIgGKpCQuAboBDwOdgYvLNJFUKU3SUph49VGkpSZx0bhPmbVsY9CRRGQvkQxcbwQecfdTgXfcXX/uSVQ1qlOdiVcdRYPayVzyxCxmLvkh6EgiEhLJwPWTwLmhL4eY2WNlG0mqooxaybw4/CiapqVw2VOzmbZofdCRRITILjcd7u4PALj7cODwso0kVVV6zWq8MLwXrQ+rwfCn5zD1q9JOEyYipRVJSWw0syFm1sLMBgPbyzqUVF1pqUk8f0Uv2mXW4upn5/DMJyvYXaS5HUWCEulU4T2BR0KfLynLQCK1UxJ59vKe9GyRxp9ens/Joz7kvYXrONDsACISfQeclgPAzMzd3cxaACsOsMZ1YDQtR+Xi7rw1fy33vbWQ5Ru206tlGn84pR2dG9cJOppIpVLauZvGAFMpvv31WGCtu58T9ZRRoJKonHbtLuKFWSsZNfUbNmwrYGBWJrec0Jam9TRBoEg0lGruJqCDu/8b6OXufYDMqKYTOYDE+DguPqo5027pz/UDWjPlq7UcO3Iad776lVa8EyljkZREoZn9P+AbM+sJaMFiCUTN5ER+e0Jbpt18DGd3a8xTM5dx9P3vM2baUvJ37Q46nkilFMnlpnSgL/AmcBSwNFZngtXlpqpl8bot3PfmQt5duJ6GtZP57QltObNrI+LjLOhoIhVKaacKzwXS3H2Hu78XqwUhVc/hGTUZf2kPnr/ySNJrVuPmSfM49aHpfLA4N+hoIpVGpHc3vefuAyLaodlUICHMt79396EHke+g6Eyi6ioqcl77cg33v72Q7zbuoG+b+vz+pCPo2Kh20NFEYl5JZxLh/jH/n30cxPHudfepYYIMCn3OAF5y975hD2iWCPwXSAPGufsTB5FBqpi4OOP0rExO7JDBs5+s5OH3vmHgIzM4s0sjfnPC4TSuqzuhRA5FJAPXAFF7isnM6gITgAOtW3k9kOPuvYHTzKxmmP0NN7McM8vJzdVlhqquWkI8l/dpwQe3HMNVR7fitS/XMODBD7jnja/ZvF33XIgcrEhLIppLh+0GhkSwz/7AxNDrmYRZ7Mjdx7p7trtnp6enRy2kVGy1qydy68lH8P7N/RnYOZOx07/l6PvfZ9z0b9lZqDuhRCIVySyw1YD7zOzoPR+lOaC757l7JAsXpQKrQq/zgIzSHFeqpkZ1qvPg4Cxev74vWU3qcNfrX3Psgx/wytxVFGlOKJEDiuRM4l3gQuCY0Ef/sgy0l61A9dDrGkR+1iPyP9pn1uLpYT155vKe1EpO5MYX53L66Blau0LkACIZuC5y9+vKPMn/mgP0AV4CsoBPAsgglUzfNun86vr6vDx3FQ+8vYgLxn1K/7bp3HryERzRoFbQ8URiTiR/nU8xs3vNrJ2ZNTWzptEOYWYDzOyXRTQB+KuZjQLaA59G+7hSNcXFGWd1a8x7N/fntpOPYM6KTZw8ajq3TJrHms07go4nElMieeL6yV+85e4+rITtJwHhRpDnuvuIiMOZZVJ8NvF2JOMYek5CDsWmbQWMfn8JT3+8grg4uLxPC67q14payYlBRxMpF6WaBXY/O2vo7muikizKVBJSGt9t3M4D7yzilbmrSUtN4oYBrbngyGYkJWg4TCq30k4V/jfgdIoHjx3Y5u5ZUU8ZBSoJiYYvv9/M3W98zcffbqBZvRRuObEtp3ZqiJnmhJLKqbRThfcBegOzKB5A1u0gUql1alyb5688kicv60FyQjzXPf85d772VdCxRAIR6bQcWRSfSXQm/HiDSKVhZhzT9jCObpPO3177iic/Wk7L9Bpc1KtZ0NFEylUkZxIXAAXAn4BrgLvKNJFIDImPM/50WnsGHHEYd0xewPRvNPWLVC2RTBW+BlhK8WJDt1L83IJIlREfZ4w6rwut02vw6+c+Y8n6rUFHEik3kUzL8XuKFxx6geKnrZ8q20gisadmciLjLsmmWkIcl0+YzUYtmypVRCSXmwa6ey9gg7s/D7Qs40wiMalJWgqPXZTNms35XP3sHAoKi4KOJFLmIimJPDO7GEg2s37ApjLOJBKzujery/3ndGbWso3c/t8vOdjnjEQqmkjubppG8WWmTcAIIL4M84jEvDO6NGJp7jYeevcbWh1Wg6v7tQo6kkiZiaQkzqT4DidNwi8SctNxbfg2dyv3vbWQFvVTObFDg6AjiZSJSC43rQOmUjxgPQENXItgZjxwbhadG9dhxItzmb8qkiVSRCqeSEoiEejk7gPc/Rh3H1DWoUQqguTEeB6/uDt1UxK5YkIO6/Lyg44kEnWRlEQGMNvM3tvzUdahRCqKw2omM+6SHuTl7+KKCTnsKNBVWalcInmYLtvdO4TOJAboTEJkX+0za/HQeV2Zv3ozv500V8uiSqWiOZBFouC49hn84eR2vPHlWkZOWRx0HJGoiXSCPxE5gCv6tmBp7lYeeX8JLdNTOatb46AjiZRa1EvCzKaWsN/v3X1otI8pEgvMjDvP6MjyDdu49d9f0jQthezmaUHHEimVsrjcdK+799/fB6HJAc0sw8yml7ST0Hra00KD5WNNK75IBZCUEMc/h3anUd3qDH9mDis3bA86kkiplPuYhJnVpfh5i9QDbHoVcE1ooLwJ0Kmss4lEQ52UJMZfks3uIufyCbPJy98VdCSRQxbEwPVuYAiQV9JG7n67u38d+rIeYVbEM7PhZpZjZjm5uZrrX2JDy/QajLmwG8t+2Mb1z39O4W5NBigVU7mXhLvnuXvEj6ea2RBggbuvDrO/saHbdLPT07VonsSO3q3r87dBHflgcS53vf71gX9AJAbF9N1NZtYSuBk4LugsIofi/J5NWbp+K+NmLKNVeioXHdU86EgiByVmSyI0dvECMOxgzjxEYs1tp7Rj2Q/buOPVr2hWL5WjD9cZr1QcMfEwnZkNMLPrfvH2rUBT4OHQXU79AogmUmrxccao87vS5rAaXPvcZyxZvyXoSCIRs2gvmmJmk4BwfyrNdfcRUT3gXrKzsz0nJ6esdi9SKt9v2s6g0TNJSYrn5Wt/RVpqUtCRRAAwsznunr3f71WmlbVUEhLrPlu5ifPGfkKXxnV45oqeVEvQGl4SvJJKIiYuN4lUFd2a1uWBc7OYtXwjf/jPfC1/KjEvZgeuRSqr07MyWbp+K6Pe/YbWh9Xgmv5a/lRil0pCJAAjjmvDtz9sCy1/msJJHRsGHUlkv3S5SSQAZsb953SmS5M63PSveVr+VGKWSkIkIMmJ8YwNLX96+YTZrN2s5U8l9qgkRAJ0WM1kxl/ag635hVzx9Gy2FxQGHUlkHyoJkYC1a1iLh87vyoLVefzmX/O0/KnEFJWESAw4tl0Gt5/SjrcWrOXBKYuCjiPyE93dJBIjLu9TvPzp6PeX0rJ+Dc7uruVPJXg6kxCJEXuWP+3dqh63/ucLZi/fGHQkEZWESCxJjI/j0Qu70bhuCldp+VOJASoJkRiz9/Knw7T8qQRMJSESg1qm12DM0G4s/2Eb1z73mZY/lcCoJERiVO9W9blrUEemf/MDf3vtq6DjSBWlu5tEYth5PZuyNHcrj09fRsv0GlzSu3nQkaSKUUmIxLhbTy5e/vSvry5g685Crjq6JQnxuggg5UP/TxOJcfFxxqjzunJyx4bc//Yizhozk0VrtQSqlI+yWL50KuHPUL5396FRPeBetDKdVHavf7GGP78yn7z8Xdx4bBuu6teKRJ1VSCmVtDJdWVxuutfdp4YJMij0OQN4yd37HmhnZtYRGOnuJ0Q3pkjFc2rnhvRqmcafJy/ggXcW89aCtTxwbhZHNKgVdDSppMr9TxAzqwtMAFIj2NaAkYBWjBcJqVejGqMv6MajF3ZjzY/5DHx4Bg+/+w27dJuslIEgzlN3A0OAvAi2vQx4v6QNzGy4meWYWU5ubm408olUCKd0asiU3/TjpI4NeXDKYgaN/oiv10Tyn5VI5Mq9JNw9z90PuAyXmdUDhgIPHGB/Y909292z09PToxVTpEJIS03i4fO78s+h3ViXl8/pj8xg1FSdVUj0xPKI173Abe6uOQlEDuCkjg2ZclM/TunUkH9MXcwZj3zEgtVaElVKL5ZLoh9wn5lNA7qY2V0B5xGJaXVTkxh1Xlceu6g767fs5IxHPuIfUxZTUKizCjl0MVESZjbAzK7b+z13P9zd+7t7f2Cuu/8xmHQiFcuJHRow5aajOa1zQ0a9+w1njNZZhRy6snhOYhIQbnBgrruPiOoB96LnJET29c6Ctdz+8nw2bSvg18e05rpjWpOUEBN/G0oMKdfnJNz93GjvU0QOzQkdGtCzRRp3vvoVD737De+Enqvo2Kh20NGkgtCfFCKVXJ2UJEYO6cK4i7PZuK2AM0Z/xMh3FmmsQiKikhCpIo5rn8GUm/pxRpdMHnpvCac/MoMvv9dYhZRMJSFShdROSWTk4C6MvySbTdsLGPToRzzw9iJ2Fu4OOprEKJWESBV0bLsM3hnRjzO7NuKR95cw8OEZfPH9j0HHkhikkhCpomqnJPLAuVk8eWkP8nYUcuajM/n7Wwt1ViH7UEmIVHHHHHEYb990NGd3a8Sj05Zy2kMzmPedziqkmEpCRKhdPZG/n5PFU5f1YOvOQs589CPue2sh+bt0VlHVqSRE5Cf92xafVZzbvQljpi3ltIdn8PnKTUHHkgCpJERkH7WSE7nvnM5MGNaTbTsLOXvMTO5582udVVRRKgkR2a9+h6fz9k1HM6RHEx774FtOfWg6n+msospRSYhIWLWSE7nnrM48PawnOwp2c86YmdwxeQEzl/7AjgKdWVQFUZ/gL0ia4E+k7GzJ38XdbyzkxdkrcYeEOKNDo9r0aFaX7OZ16d4sjfSa1YKOKYegpAn+VBIiclA2b9/FZys3MXv5RnJWbGLudz/+NA9Ui/qpdG9Wlx7N65LdPI2W9VMpXqpeYplKQkTKzM7C3cxflcecFRuZvXwTOcs3sml78YKSaalJ+5RGx8zamqo8BqkkRKTcuDtLc7ftUxrLN2wHoFpCHFlN6hSXRrM0ujWrS+3qiQEnFpWEiAQqd8vOn0tjxSYWrNpMYZFjBm0zaobONtLIbl6XRnWq6xJVOVNJiEhM2V5QyNzvfiQnVBqfrdjE1p2FADSsnbxPaRzRoBbxcSqNslSuK9OZ2dQS9vu9uw+N9jFFpGJJSUqgd6v69G5VH4DdRc7CtXnMWbHpp0tUr32xBoAa1RLo2rTOT6XRpUkdUpKi/k+XhFEWa1wf5+5Tw3xvkLu/bGYZwEvu3jeC/U0G/uLunx9oW51JiFQeq37cQc7yjcV3US3fxKJ1W3CH+DijY2Yt2jaoSfXEeJL3+Yj7+XNC8XvV9ryXsPf3f94mTmcp5XsmEUGYusAEIDWCbS8Evi2pIMxsODAcoGnTptGKKSIBa1SnOo26NOKMLo0A2Lyj+Nbb4uLYxAeLc8nfVcSOXbtLtRRrUkIcyQlx/1s0CXsVTGL8XtvE7VNMrQ+rQe9W9UiMr5x3bZX7mQTwHmDAK+7ev4T9pAFfAWOAD939/QMdW2cSIlVTUZGzs7CI/F27yS/cTf6u0OtdodeFu9m56xfv79k+9N7O0M/tKNizj72/97/72/ufzrTUJE7u2IDTs0FiJfcAAActSURBVDLp0Tytwp2dxNSZhLvnAZHcvXATMAl4DLjHzGq6++QyjiciFVBcnFE9KZ7qSfHlcjx3p2B3caHMWraRV79Yw38+W8Vzn66kQa1kTuvckNO7ZNKpUe0Kf6dWLI/+dAVudve1ZjYROB5QSYhI4MyMagnxVEuI54QODTihQwO27Sxk6tfreHXeaiZ8vJxxM5bRvF4KA7MyOT0rkzYZNYOOfUhiuSSWAC2BhUA2sCLYOCIi4aVWS+CM0BjK5u27eGvBGibPW83o95fw8HtLOKJBzZ8Ko0laStBxIxbI3U2h19P2jEmY2QCgvbs/ste2mcA4oDawHTjL3beUdGyNSYhIrFm/JZ83vigujM9WFi8L27VpHQZ2zuS0zg05rFZywAnL+WE6M5sEpIf59lx3HxHVA+5FJSEisey7jdt5LVQYX6/JI86gV8t6DMzK5OSODaiTkhRILj1xLSISY5as38LkeWt4dd5qlv2wjcR44+g26QzMyuT49hmkViu/0QCVhIhIjHJ3FqzOY/K81bw6bzVrNueTnBjHsUdkMDArk/5t00lOLNu7tlQSIiIVQFGRM2flJibPXc0bX65hw7YCalZL4MSODRiYlcmvWtUjoQwe2lNJiIhUMIW7i5i5dAOT563m7flr2bKzkHqpSZzSqSEDszLJblY3ag/tqSRERCqw/F27+WBxLpPnrebdr9eRv6uIzNrJnJaVycDOmXRsVKtUD+2pJEREKok9D+1NnruaD7/JZddup0X9VH7dvxXnZjc5pH3G1LQcIiJy6PZ+aO/H7QW8NX8tk+etZmcpJjksiUpCRKSCqpOSxHk9m3Jez7KbAbtyzm0rIiJRoZIQEZGwVBIiIhKWSkJERMJSSYiISFgqCRERCUslISIiYakkREQkrEo1LYeZ5VLxlzmtD/wQdIgYot/HvvT7+Jl+F/sqze+jmbvvd7G4SlUSlYGZ5YSbQ6Uq0u9jX/p9/Ey/i32V1e9Dl5tERCQslYSIiISlkog9Y4MOEGP0+9iXfh8/0+9iX2Xy+9CYhIiIhKUzCRERCUslISIiYakkYoSZ1TazN81sipn918ySgs4UNDPLMLPPg84RK8zsUTMbGHSOoJlZXTN7w8ymm9k/g84TpNB/I9NDrxPN7DUzm2lmw6J1DJVE7LgQGOnuxwNrgZMCzhMLHgCqBx0iFphZX6CBu78adJYYcBHwrLv3BWqaWZV8VsLM6gITgNTQW9cDOe7eGzjNzGpG4zgqiRjh7o+6+5TQl+nA+iDzBM3MBgDbKC7MKs3MEoHHgeVmdkbQeWLABqCtmdUBmgArA84TlN3AECAv9HV/YGLo9UwgKuWpkogxZnYUUNfdPwk6S1BCl9r+DNwadJYYcTHwFfB3oKeZXR9wnqDNANoANwALgU3BxgmGu+e5++a93koFVoVe5wEZ0TiOSiKGmFka8DAQteuJFdStwGh3/zHoIDGiKzDW3dcCzwLHBJwnaHcDV7v7nRSXxGUB54kVW/n58mwNovTvu0oiRoT+ep4I3ObuFX2SwtI6DrjWzKYBXcxsXMB5grYEaBl6nU3Fn8SytFKATmYWDxwJ6GGvYnOAPqHXWcDyaOxUD9PFCDO7huK/kOaF3hrj7v8KMFJMMLNp7t4/6BxBCg1APkHx5YNE4Bx3X1XyT1VeZtYTeBJoBnwMnOnuW4NNFZw9/42YWTPgDWAq0Bvo5e67S71/lYSISOVgZpkUn028/YvxikPfp0pCRETC0ZiEiIiEpZIQEZGwVBIiIhKWSkJERMJSSYiUAzN7ysyaH+TPdDGzLnt9fYeZ9Y9yNJESqSREYleX0IdIYHQLrMhezGwOxZMrFgANgFeAEwADZrj77WZ2IdAN+AvwIdDP3bfsZ18tgOeA7UAtYDCwA3gKqA286u73mNlToe9nAJ+7+3Vmdg9wZmhXq9z9WDO7g+KH6fqEfv6k0FQdImVGZxIi+0oBzgU6AxcAxwN/BE4FBgK4+3NAB2AUcN/+CiLkdxRPyncSsGfa5tuAf4Wmcx5kZvVC77/k7r8CWphZd3e/DbgXuNfdj91rn63dvR/wPDAgGv+DRUqikhDZ17rQFA8rKJ6KOY/iCQfH8fM/9IS+Pg2YVMK+WgBfuHshMDf0XlvgmtC8VKlAZuj9OaHPXwDNS9jn06HP64EqvzCVlD2VhEjJ8oB7gCsITSRnZgkUT1P9DHBtCT+7AmgfmoiuU+i9RcCtofmo7gU2ht7vGfrcheIJ/aD40lRK6JgWem9b6f7niBwclYRIyc4B/glMBrabWSNgBPAaxZeOhppZ4zA/ez/Fl6qmUDzGAcXFcLOZfUTxZah1ofdPC7230N33TPI4BTgr9H7f6P7PEomMBq5FAhYauL7D3ZcHHEXkf6gkRErJzBoAL/7i7UXuflUQeUSiSSUhIiJhaUxCRETCUkmIiEhYKgkREQlLJSEiImH9fxT/126KLUUlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#决策树预测房价\n",
    "#和KNN类似，这里没法确定决策树的深度，因此令最大深度分别是1至10。\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "score=[]\n",
    "for n in range(1,11):\n",
    "    dtr = DecisionTreeRegressor(max_depth = n)\n",
    "    dtr_predict = cross_val_predict(dtr, x_train, y_train, cv=5)\n",
    "    dtr_score = cross_val_score(dtr, x_train, y_train, cv=5)\n",
    "    dtr_meanscore = dtr_score.mean()\n",
    "    score.append(dtr_meanscore)\n",
    "plt.plot(np.linspace(1,10,10), score)\n",
    "plt.xlabel('max_depth')\n",
    "plt.ylabel('mean-score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#发现depth为5的时候，分数较高，因此取3\n",
    "n=1\n",
    "dtr = DecisionTreeRegressor(max_depth = n)\n",
    "dtr_predict = cross_val_predict(dtr, x_train, y_train, cv=5)\n",
    "dtr_score = cross_val_score(dtr, x_train, y_train, cv=5)\n",
    "dtr_meanscore = dtr_score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lr</th>\n",
       "      <th>linear_svr</th>\n",
       "      <th>poly_svr</th>\n",
       "      <th>rbf_svr</th>\n",
       "      <th>knn</th>\n",
       "      <th>dtr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.140950</td>\n",
       "      <td>-0.086769</td>\n",
       "      <td>-0.204454</td>\n",
       "      <td>-0.016258</td>\n",
       "      <td>0.066297</td>\n",
       "      <td>0.050244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.020709</td>\n",
       "      <td>0.031534</td>\n",
       "      <td>-0.013258</td>\n",
       "      <td>-0.024012</td>\n",
       "      <td>0.000758</td>\n",
       "      <td>0.089787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.359273</td>\n",
       "      <td>-0.385375</td>\n",
       "      <td>-0.436985</td>\n",
       "      <td>-0.464115</td>\n",
       "      <td>-0.425998</td>\n",
       "      <td>-0.318835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.502168</td>\n",
       "      <td>-0.602023</td>\n",
       "      <td>-0.683578</td>\n",
       "      <td>-0.599131</td>\n",
       "      <td>-0.705812</td>\n",
       "      <td>-0.329519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.089132</td>\n",
       "      <td>-0.069343</td>\n",
       "      <td>-0.410695</td>\n",
       "      <td>-0.345163</td>\n",
       "      <td>-0.148846</td>\n",
       "      <td>-0.552322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lr  linear_svr  poly_svr   rbf_svr       knn       dtr\n",
       "0 -0.140950   -0.086769 -0.204454 -0.016258  0.066297  0.050244\n",
       "1 -0.020709    0.031534 -0.013258 -0.024012  0.000758  0.089787\n",
       "2 -0.359273   -0.385375 -0.436985 -0.464115 -0.425998 -0.318835\n",
       "3 -0.502168   -0.602023 -0.683578 -0.599131 -0.705812 -0.329519\n",
       "4 -0.089132   -0.069343 -0.410695 -0.345163 -0.148846 -0.552322"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#接下来把分数汇总\n",
    "evaluating = {\n",
    "        'lr':lr_score,\n",
    "        'linear_svr':linear_svr_score,\n",
    "        'poly_svr':poly_svr_score,\n",
    "        'rbf_svr':rbf_svr_score,\n",
    "        'knn':knn_score,\n",
    "        'dtr':dtr_score\n",
    "        }\n",
    "evaluating = pd.DataFrame(evaluating)\n",
    "evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a1f2b3d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a2027910>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a204f160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a212f940>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a2154160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a21877c0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimizer = {\n",
    "        'lr':lr_score,\n",
    "        'linear_svr':linear_svr_score,\n",
    "        'poly_svr':poly_svr_score,\n",
    "        'rbf_svr':rbf_svr_score,\n",
    "        'knn':knn_score,\n",
    "        'dtr':dtr_score\n",
    "        }\n",
    "optimizer= pd.DataFrame(optimizer)\n",
    "optimizer.hist(color='k',alpha=0.6,figsize=(8,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>-0.017885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rbf_svr</th>\n",
       "      <td>-0.044626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>-0.048580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear_svr</th>\n",
       "      <td>-0.056439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dtr</th>\n",
       "      <td>-0.080174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly_svr</th>\n",
       "      <td>-0.086683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               score\n",
       "knn        -0.017885\n",
       "rbf_svr    -0.044626\n",
       "lr         -0.048580\n",
       "linear_svr -0.056439\n",
       "dtr        -0.080174\n",
       "poly_svr   -0.086683"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#开始预测\n",
    "#rbf\n",
    "rbf_svr.fit(x_train,y_train)\n",
    "rbf_svr_y_predict = rbf_svr.predict(x_test)\n",
    "rbf_svr_y_predict_score=rbf_svr.score(x_test, y_test)\n",
    "#KNN\n",
    "knn.fit(x_train,y_train)\n",
    "knn_y_predict = knn.predict(x_test)\n",
    "knn_y_predict_score = knn.score(x_test, y_test)\n",
    "#poly_svr\n",
    "poly_svr.fit(x_train,y_train)\n",
    "poly_svr_y_predict = poly_svr.predict(x_test)\n",
    "poly_svr_y_predict_score = poly_svr.score(x_test, y_test)\n",
    "#dtr\n",
    "dtr.fit(x_train, y_train)\n",
    "dtr_y_predict = dtr.predict(x_test)\n",
    "dtr_y_predict_score = dtr.score(x_test, y_test)\n",
    "#lr\n",
    "lr.fit(x_train, y_train)\n",
    "lr_y_predict = lr.predict(x_test)\n",
    "lr_y_predict_score = lr.score(x_test, y_test)\n",
    "#linear_svr\n",
    "linear_svr.fit(x_train, y_train)\n",
    "linear_svr_y_predict = linear_svr.predict(x_test)\n",
    "linear_svr_y_predict_score = linear_svr.score(x_test, y_test)\n",
    "predict_score = {\n",
    "        'lr':lr_y_predict_score,\n",
    "        'linear_svr':linear_svr_y_predict_score,\n",
    "        'poly_svr':poly_svr_y_predict_score,\n",
    "        'rbf_svr':rbf_svr_y_predict_score,\n",
    "        'knn':knn_y_predict_score,\n",
    "        'dtr':dtr_y_predict_score\n",
    "        }\n",
    "predict_score = pd.DataFrame(predict_score, index=['score']).transpose()\n",
    "predict_score.sort_values(by='score',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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